Reversible Data Hiding for Color Images Based on Adaptive 3D Prediction-Error Expansion and Double Deep Q-Network

Chang, J. Morris; Zhu, Guopu; Zhang, Hongli; Zhou, Yicong; Luo, Xiangyang; Wu, Ligang

doi:10.1109/tcsvt.2022.3146517

Cited by 10 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By utilizing the similarities between the nearby pixels, a more efficient modification method can be developed for the two-dimensional prediction-error histogram (2D-PEH), which has a lower entropy than the one-dimensional (1D) PEH. In the work by Chang et al [22], a high computational complexity RDH technique was proposed based on adaptive threedimensional PEE and a double-deep Q-network (DQN). In which DDQN was adopted to direct the modification directions, identify the best PEE mapping paths, and give a scheme for action selection to guide modification directions so that it could quickly discover the reversible mapping paths.…”

Section: Related Workmentioning

confidence: 99%

“…Usually, the prediction error histogram is used in conjunction with histogram modification to create prediction-error histograms (PEHs), from which the appropriate expansion bins for histogram shifting and histogram expansion are chosen. To address these flaws, researchers proposed various techniques and methods [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] capable of reducing the risks associated with RDH based on a histogram in which the number of valid shifting pixels is determined for embedding based on embedding capacity and increasing embedding capacity with low distortion by enhancing the use of color image features such as color, contrast, edges, and textures, but they solved each problem separately, which leads to exacerbation of another one.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

et al. 2023

View full text Add to dashboard Cite

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel’s local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In this situation, the distortion of the histogram-based RDH is only related to the histogram of X. Most subsequent histogram-based methods follow this direc-tion, and the embedding process includes two steps: histogram generation and histogram modification [8][9][10][11][12][13][14].…”

Section: Traditional Rdh Frameworkmentioning

confidence: 99%

“…For grayscale and color images, many RDH methods have been developed in recent years [8][9][10][11][12][13][14]. Early RDH methods aimed to release redundant space by losslessly compressing the cover image features.…”

Section: Introductionmentioning

confidence: 99%

High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Li,

Xiao

et al. 2023

Mathematics

View full text Add to dashboard Cite

Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding data into visually insensitive regions (e.g., edges and textures). In this paper, following this direction, a high-capacity RDH approach based on multi-embedding is proposed. The cover image is decoupled to select the embedding sequence that can better utilize texture pixels and reduce the size of the reconstruction information, and a multi-embedding strategy is proposed to embed the secret data along with the reconstruction information by matrix embedding. The experimental results demonstrate that the proposed method provides a superior visual quality and higher embedding capacity than some state-of-the-art RDH works for binary images. With an embedding capacity of 1000 bits, the proposed method achieves an average PSNR of 49.45 dB and an average SSIM of 0.9705 on the test images. This marks an improvement of 1.1 dB in PSNR and 0.0242 in SSIM compared to the latest state-of-the-art RDH method.

show abstract

Optimized reversible data hiding technique based on multidirectional prediction error histogram and fluctuation-based adaptation

Kasasbeh,

Anbar

2024

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

Reversible Data Hiding for Color Images Based on Adaptive 3D Prediction-Error Expansion and Double Deep Q-Network

Cited by 10 publications

References 35 publications

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Optimized reversible data hiding technique based on multidirectional prediction error histogram and fluctuation-based adaptation

Contact Info

Product

Resources

About